社交机器人语言学习中机器人方言的相似性吸引

Askarbek Pazylbekov, Daryn Kalym, Anuar Otynshin, A. Sandygulova
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引用次数: 4

摘要

哈萨克斯坦共和国逐渐向哈萨克语过渡,提出了应用新技术来学习哈萨克语。考虑到哈萨克语具有方言形式的事实,研究这些语言特征如何影响与机器人或计算机程序的合成语音的交互是很重要的。本文对以教育为导向的环境中方言语言对人机交互的影响进行了初步研究。为了学习新词汇,参与者与两个不同的机器人(南方和非南方)进行互动,这些机器人具有预先编程的语言方言模式。研究结果显示相关性不显著,但是,这表明样本量小导致了得到的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Similarity Attraction for Robot's Dialect in Language Learning Using Social Robots
The gradual transition towards the Kazakh language in the Republic of Kazakhstan raises the emergence of applying new technologies for learning the language. Considering the fact that the Kazakh language has dialectal forms, it is important to investigate how these language features would affect the interaction with the synthesized speech of a robot or a computer program. This paper presents a preliminary study exploring the effect of dialectal language on the human-robot interaction in an education-oriented environment. Participants were involved in the interaction with two different robots with pre-programmed language dialectal patterns - South and non-South, to learn new vocabulary. Findings show that there is a low significance in correlation, however, it is suggested that a small sample size led to the obtained results.
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